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    "### Text preprocessing, TF-IDF representation and scatter visualization"
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   "execution_count": 3,
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       "            </script>\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import texthero as hero\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\n",
    "   \"https://github.com/jbesomi/texthero/raw/master/dataset/bbcsport.csv\"\n",
    ")\n",
    "\n",
    "df['pca'] = (\n",
    "   df['text']\n",
    "   .pipe(hero.clean)\n",
    "   .pipe(hero.tfidf)\n",
    "   .pipe(hero.pca)\n",
    ")\n",
    "fig = hero.scatterplot(df, 'pca', color='topic', title=\"PCA BBC Sport news\", return_figure=True)\n",
    "fig.write_image(\"../github/scatterplot_bbcsport.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text preprocessing, TF-IDF, K-means and visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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\"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"K-means BBC Sport news\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"x\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"y\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('bbcd20a5-d284-490c-84eb-80e252463503');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import texthero as hero\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\n",
    "    \"https://github.com/jbesomi/texthero/raw/master/dataset/bbcsport.csv\"\n",
    ")\n",
    "\n",
    "df['tfidf'] = (\n",
    "    df['text']\n",
    "    .pipe(hero.clean)\n",
    "    .pipe(hero.tfidf)\n",
    ")\n",
    "\n",
    "df['kmeans_labels'] = (\n",
    "    df['tfidf']\n",
    "    .pipe(hero.kmeans, n_clusters=5)\n",
    "    .astype(str)\n",
    ")\n",
    "\n",
    "df['pca'] = (\n",
    "    df['tfidf']\n",
    "    .pipe(hero.pca)\n",
    ")\n",
    "\n",
    "fig = hero.scatterplot(df, 'pca', color='kmeans_labels', title=\"K-means BBC Sport news\", return_figure=True)\n",
    "fig.write_image(\"../github/scatterplot_bbcsport_kmeans.svg\")"
   ]
  }
 ],
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